Citrus is a very important crop in Florida. In the 2010-2011 season, 7.4 million tons of citrus were produced in Florida which included 63 percent of the total United States citrus production, as reported by Putnam (Putnam, A. H. 2012, “Florida Agriculture by the Numbers” in, Florida Department of Agriculture and Consumer Services, Tallahassee, Fla.) (Putnam 2012). Citrus greening or Huanglongbing (HLB), also known as yellow shoot disease, is a very severe disease which has decreased the citrus production in Florida. The disease is caused by the insect-vectored α-protobacterium Candidiatus Liberibacter Asiaticus, as reported by Mishra, et al. (Mishra, A., R. Ehsani, G. Albrigo, and W. S. Lee, 2007, “Spectral Characteristics of Citrus Greening (Huanglongbing)” in, ASABE Paper No. 073056, Minneapolis, Minn.: ASABE) (Mishra, et al. 2007). Blotchy mottle on leaf, yellow shoots, inverted color, and uneven fruits are some of the disease symptoms; however, it is unlikely that they would appear altogether in the same tree. The disease reduces the production, degrades the fruit quality and finally destroys the tree, as reported by Gonzalez, et al. (Gonzalez, P., J. Reyes, DeCorcuera, and Etxeberria, E., 2012, “Characterization of leaf starch from HLB-affected and unaffected-girdled citrus trees” in, Physiological and Molecular Plant Pathology, 79: 71-78) (Gonzalez, et al. 2012). Although, no practical treatment has been reported for the disease yet, detecting and removing the infected trees can avoid spreading the infection to the other trees.
Many studies have focused on HLB detection and several methods have been tried for this purpose. In 1996, polymerase chain reaction (PCR) method was proven to be an effective HLB detection method, as reported by Hocquellet, et al. (Hocquellet, A., P. Toorawa, J. M. Bove, and M. Gamier, 1999, “Detection and identification of the two Candidatus Liberobacter species associated with citrus huanglongbing by PCR amplification of ribosomal protein genes of the beta operon” in, Mol. Cell Probes, 373-379. England: 1999 Academic Press) (Hocquellet, et al. 1999). Li, et al. (Li, W., J. S. Hartung, and L. Levy, 2006, “Quantitative real-time PCR for detection and identification of Candidatus Liberibacter species associated with citrus huanglongbing” in, Journal of Microbiological Methods, 66(1): 104-115)(Li, et al. 2006) also developed a real-time and quantitative PCR assay method and examined it successfully for HLB confirmation in Florida. However, the PCR method is a laboratory based approach which is expensive and time consuming, and so cannot be used in a real-time in-field application. Currently, growers try to find noticeable HLB symptoms to identify the infected trees. However, it is not easy to differentiate the symptoms resulting from nutrient deficiency and HLB.
Early and quick detection has been considered widely in recent studies. The use of near-infrared and mid-infrared spectroscopy, for example, has been investigated to identify the HLB infected trees from healthy or nutrient deficient ones under laboratory and field conditions, as reported by Mishra, et al. (Mishra, et al. 2007); Mishra, et al. (Mishra, A., D. Karimi, R. Ehsani, and L. G. Albrigo, 2011, “Evaluation of an active optical sensor for detection of Huanglongbing (HLB) disease” in, Biosystems Engineering, 110: 302-309) (Mishra, et al. 2011); Mishra, et al. (Mishra, A. R., D. Karimi, R. Ehsani, and W. S. Lee, 2012, “Identification of citrus greening (HLB) using a VIS-NIR spectroscopy technique” in Trans. ASABE, 55(2): 711-720) (Mishra et al. 2012); Sankaran and Eshani (Sankaran and Eshani, 2011, “Visible-near infrared spectroscopy based citrus greening detection: Evaluation of spectral feature extraction techniques” in Crop Protection, 30: 1508-1513) (Sankaran and Ehsani 2011); Sankaran, et al. (Sankaran, S., R. Ehsani, and E. Etxeberria, 2010, “Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves” in Talanta, 83: 574-581)(Sankaran, et al. 2010); and, Sankaran, et al. (Sankaran, S., A. Mishra, J. M. Maja, and R. Ehsani, 2011, “Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards” in, Computers and Electronics in Agriculture, 77(2): 127-134)(Sankaran, et al. 2011).
Airborne hyperspectral and multispectral imaging approaches have also been employed in HLB disease detection, as reported by Kumar, et al. (Kumar, A., W. S. Lee, R. J. Ehsani, L. G. Albrigo, C. H. Yang, and R. L. Mangan, 2012, “Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques” in Journal of Applied Remote Sensing, 6(1)) (Kumar, et al. 2012); Li, et al. (Li, H., W. S. Lee, R. Wang, R. Ehsani, and C. Yang, 2012, “Spectral angle mapper (SAM) based citrus greening disease detection using airborne hyperspectral imaging” in, 11th International Conference on Precision Agriculture, Indianapolis, Ind.) (Li, Lee and Wang, et al. 2012); Li, et al. (Li, X., W. S. Lee, M. Li, R. Ehsani, A. R. Mishra, C. Yang, and R. L. Mangan, 2011, “Comparison of different detection methods for citrus greening disease based on airborne multispectral and hyperspectral imagery” in ASABE Paper No. 1110570, Louisville, Ky.: ASABE)(Li, et al. 2011); and, Li, et al. (Li, X. H., W. S. Lee, M. Z. Li, R. Ehsani, A. R. Mishra, C. H. Yang, and R. L. Mangan, 2012, “Spectral difference analysis and airborne imaging classification for citrus greening infected trees” in, Computers and Electronics in Agriculture, 83: 32-46) (Li, Lee, and Li, et al. 2012). Based on their results, the difference between the reflectance signatures of HLB and healthy trees can be used to highlight the severely symptomatic areas. Some researchers examined the capability of laser-induced fluorescence spectroscopy and imaging for citrus disease identification, as reported by Belasque, Jr., et al. (Belasque, Jr., J., M. Gasparoto, and L. Marcassa, 2008, “Detection of mechanical and disease stresses in citrus plants by fluorescence spectroscopy” in, Applied Optics 47(11): 1922-1926) (Belasque, Jr., et al. 2008); Lins, et al. (Lins, E. C., J. Belasque Jr, and L. G. Marcassa, 2009, “Detection of citrus canker in citrus plants using laser induced fluorescence spectroscopy” in, Precision Agriculture, 10(4): 319-330) (Lins, et al. 2009); and, Marcassa, et al. (Marcassa, L., M. Gasparoto, J. Belasque Jr, E. Lins, F. D. Nunes, and V. Bagnato, 2006, “Fluorescence spectroscopy applied to orange trees” in, Laser Physics, 16(5): 884-888) (Marcassa, et al. 2006). Pereira, et al. (Pereira, F. M. V., D. M. B. P. Milori, E. R. Pereira-Filho, A. L. Venincio, M. D. S. T. Russo, M. C. D. B. Cardinali, P. K. Martins, and J. Freitas-Astúa, 2011, “Laser-induced fluorescence imaging method to monitor citrus greening disease” in Computers and Electronics in Agriculture, 79: 90-93) (Pereira, et al. 2011), for instance, achieved an accuracy of 95% for detection of HLB symptomatic in early stages from healthy samples. Microscopic images from the citrus leaves turned out to be a capable method to identify the HLB symptoms from nutrient deficiencies, as reported by Kim, et al. (Kim, D. G., T. F. Burks, A. W. Schumann, M. Zekri, X. Zhao, and Q. Jianwei, 2009, “Detection of Citrus Greening Using Microscopic Imaging” in, Agricultural Engineering International: the CIGR Ejournal) (Kim, et al. 2009). Kim, et al. (Kim, et al. 2009) extracted color co-occurrence features from the leaf images and obtained the overall accuracy of 97% using a selected feature-set and a discriminant classifier.
Gonzalez et al. (Gonzalez, et al. (2012) proved that the starch content in HLB-infected leaves increases compared to the healthy ones. Their results indicated that the accumulated starch in HLB symptomatic leaves were not biochemically similar to the healthy leaves' starch which was accumulated as a result of a mechanical injury. Therefore, the starch measurement in citrus leaves can be considered as a HLB detection method. Furthermore, starch was determined to be able to rotate the polarization planer of light by McMahon (McMahon, K. A., 2004, “Practical Botany—The Maltese Cross” in, Tested Studies for Laboratory Teaching, 25: 352-357) (McMahon, 2004). This capability of starch on polarized light was previously evaluated by the authors and an imaging system which was able to highlight the starch accumulated on HLB leaf was developed. The system was examined for Hamlin variety of citrus in four classes of healthy, HLB symptomatic, zinc deficient, and HLB symptomatic-zinc deficient samples and the overall accuracy of 90% was achieved. The classification rate increased to 93% when the HLB detection was considered as the purpose of the classification.